U.S. patent application number 17/142699 was filed with the patent office on 2021-07-01 for real-time intelligent ran controller to support self-driving open ran.
The applicant listed for this patent is Joey Chou, Puneet Jain, Leifeng Ruan. Invention is credited to Joey Chou, Puneet Jain, Leifeng Ruan.
Application Number | 20210204148 17/142699 |
Document ID | / |
Family ID | 1000005504254 |
Filed Date | 2021-07-01 |
United States Patent
Application |
20210204148 |
Kind Code |
A1 |
Chou; Joey ; et al. |
July 1, 2021 |
REAL-TIME INTELLIGENT RAN CONTROLLER TO SUPPORT SELF-DRIVING OPEN
RAN
Abstract
Systems for providing SON functions running on an O-RAN RIC are
described. To provide resource allocation for a non-real time RIC,
the SON function monitors at least one RAN through collection of
data from a network function. The data is analyzed to determine
whether a network issue is present and, if so, is to be resolved.
The SON function determines a SON action to be executed to resolve
the network issue, and subsequently executes the SON action.
Performance measurements of the network function are collected
after execution of the SON action. The performance measurements are
analyzed to evaluate whether the SON action has resolved the
network issues and, if the network issues are not resolved, further
SON actions are determined and subsequently executed.
Inventors: |
Chou; Joey; (Scottsdale,
AZ) ; Jain; Puneet; (Hillsboro, OR) ; Ruan;
Leifeng; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Chou; Joey
Jain; Puneet
Ruan; Leifeng |
Scottsdale
Hillsboro
Beijing |
AZ
OR |
US
US
CN |
|
|
Family ID: |
1000005504254 |
Appl. No.: |
17/142699 |
Filed: |
January 6, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62959721 |
Jan 10, 2020 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W 74/0833 20130101;
H04W 84/042 20130101; H04W 84/18 20130101; H04W 24/02 20130101 |
International
Class: |
H04W 24/02 20060101
H04W024/02; H04W 74/08 20060101 H04W074/08 |
Claims
1. An apparatus configured to operate as a radio access network
(RAN) Intelligent Controller (RIC) in a service management and
orchestration framework of a new radio (NR) network, the apparatus
comprising: processing circuitry configured to provide a
self-organizing network (SON) function, the SON function configured
to: monitor a RAN through collection of data from a network
function; analyse the data to determine whether network issue is
present and is to be resolved; determine a SON action to be
executed to resolve the network issue; and interact with the
network function to execute the SON action; and memory configured
to store the data.
2. The apparatus of claim 1, wherein the RIC is a non-real time
RIC.
3. The apparatus of claim 1, wherein the RIC is configured to
communicate with a near-real time RIC.
4. The apparatus of claim 1, wherein: the RIC is configured to
collect the data from the network function via an O1 interface, and
the network function comprises an open RAN (O-RAN) central unit
user plane (O-CU-UP), an O-RAN central unit control plane
(O-CU-CP), an O-RAN distributed unit (O-DU) and an O-RAN remote
unit (O-RU).
5. The apparatus of claim 1, wherein the processing circuitry is
further configured to collect the data over an extended time period
to predict traffic demand patterns of the RAN at different times
and locations and re-allocate network resources prior to network
issues being detected.
6. The apparatus of claim 5, wherein the processing circuitry is
further configured to analyze the data to at least one of generate
or train an artificial intelligence/machine learning model related
to resource allocation.
7. The apparatus of claim 1, wherein the data comprises performance
measurements and analytic data.
8. The apparatus of claim 7, wherein the processing circuitry is
further configured to: collect performance measurements of the
network function after execution of the SON action, analyze the
performance measurements to evaluate whether the SON action has
resolved the network issues, in response to an evaluation that the
network issues have not been resolved by the SON action, determine
further SON actions to resolve the network issues, and execute the
further SON actions.
9. The apparatus of claim 8, wherein the performance measurements
comprise data volume, a number of registered user equipment (UEs),
and a number of protocol data unit (PDU) sessions.
10. The apparatus of claim 1, wherein the processing circuitry is
configured use the SON function to optimize random access channel
(RACH) performance over a plurality of NR cells through
configuration of RACH parameters and adaption to changes in at
least one of a number of user equipment (UEs) or resources in at
least one of the NR cells.
11. A computer-readable storage medium that stores instructions for
execution by one or more processors configured to operate as a
radio access network (RAN) Intelligent Controller (RIC) in a
service management and orchestration framework of a new radio (NR)
network, the instructions when executed configure the one or more
processors to: collect, at a self-organizing network (SON) function
via an O1 interface, data from network functions, the network
functions comprising an open RAN (O-RAN) central unit user plane
(O-CU-UP), an O-RAN central unit control plane (O-CU-CP), an O-RAN
distributed unit (O-DU) and an O-RAN remote unit (O-RU); analyse,
within the SON function, the data to determine whether at least one
network issue is present in at least one of the network functions;
and execute at least one self-organizing network (SON) action to
resolve the at least one network issue.
12. The medium of claim 11, wherein the RIC is a non-real time
RIC.
13. The medium of claim 11, wherein the instructions when executed
further configure the one or more processors to collect the data
over an extended time period to predict traffic demand patterns at
different times and locations and re-allocate network resources
prior to network issues being detected.
14. The medium of claim 13, wherein the instructions when executed
further configure the one or more processors to analyze the data to
at least one of generate or train an artificial
intelligence/machine learning model related to resource
allocation.
15. The medium of claim 1, wherein the instructions when executed
further configure the one or more processors to: collect
performance measurements of the O-CU-UP, O-CU-CP, O-DU and O-RU
after execution of the at least one SON action, analyze the
performance measurements to evaluate whether the at least one SON
action have resolved the at least one network issue, in response to
an evaluation that the network issues have not been resolved by the
SON actions, execute further SON actions to resolve the at least
one network issue.
16. The medium of claim 15, wherein the performance measurements
comprise data volume, a number of registered user equipment (UEs),
and a number of protocol data unit (PDU) sessions.
17. The medium of claim 11, wherein the instructions when executed
further configure the one or more processors to optimize random
access channel (RACH) performance over a plurality of NR cells
through configuration of RACH parameters and adaption to changes in
at least one of a number of user equipment (UEs) or resources in at
least one of the NR cells.
18. A computer-readable storage medium that stores instructions for
execution by one or more processors configured to operate as a
network function of a 5.sup.th generation NodeB (gNB), the
instructions when executed configure the one or more processors to:
send performance measurements of the network function, to a
self-organizing network (SON) function in a network (RAN)
Intelligent Controller (RIC) in a service management and
orchestration framework via an O1 interface, the performance
measurements including uplink and downlink data traffic and a
number of protocol data unit (PDU) sessions, the performance
measurements providing an indication of a network issue to resolve
at the network function; and receive, from the SON function, a SON
action to resolve the network issue.
19. The medium of claim 18, wherein the instructions when executed
further configure the one or more processors to send the
performance measurements over an extended time period to permit a
prediction of traffic demand patterns at different times for the
network function and re-allocate network resources associated with
the network function prior to the network issue at the network
function being indicated.
20. The medium of claim 19, wherein the RIC is a non-real time RIC.
Description
PRIORITY CLAIM
[0001] This application claims the benefit of priority under 35
U.S.C. 119(e) to U.S. Provisional Patent Application Ser. No.
62/959,721, filed Jan. 10, 2020, which is incorporated herein by
reference in its entirety.
TECHNICAL FIELD
[0002] Embodiments pertain to radio access networks (RANs). Some
embodiments relate to controllers in self-organizing network (SON)
functions. Some embodiments relate to intelligent controllers in
RANs running SON functions.
BACKGROUND
[0003] The use and complexity of 3GPP LTE systems (including LTE
and LTE-Advanced systems) has increased due to both an increase in
the types of devices user equipment (UEs) using network resources
as well as the amount of data and bandwidth being used by various
applications, such as video streaming, operating on these UEs. With
the vast increase in number and diversity of communication devices,
the corresponding network environment, including routers, switches,
bridges, gateways, firewalls, and load balancers, has become
increasingly complicated, especially with the advent of next
generation (NG) (or new radio (NR)/5.sup.th generation (5G))
systems. As expected, a number of issues abound with the advent of
any new technology.
BRIEF DESCRIPTION OF THE FIGURES
[0004] In the figures, which are not necessarily drawn to scale,
like numerals may describe similar components in different views.
Like numerals having different letter suffixes may represent
different instances of similar components. The figures illustrate
generally, by way of example, but not by way of limitation, various
embodiments discussed in the present document.
[0005] FIG. 1A illustrates an architecture of a network, in
accordance with some aspects.
[0006] FIG. 1B illustrates a non-roaming 5G system architecture in
accordance with some aspects.
[0007] FIG. 1C illustrates a non-roaming 5G system architecture in
accordance with some aspects.
[0008] FIG. 2 illustrates a block diagram of a communication device
in accordance with some embodiments.
[0009] FIG. 3 illustrates an NFV network management architecture in
accordance with some embodiments.
[0010] FIG. 4 illustrates a self-organizing network (SON) function
over a RAN Intelligent Controller (RIC) in accordance with some
embodiments.
[0011] FIG. 5 illustrates a random access channel (RACH)
optimization in accordance with some embodiments.
[0012] FIG. 6 illustrates the realization of a SON function on the
Non Real-Time RIC in accordance with some embodiments.
[0013] FIG. 7 illustrates Network Slice Instance (NSI) resource
allocation optimization in accordance with some embodiments.
[0014] FIG. 8 illustrates another realization of a SON function on
the Non Real-Time RIC in accordance with some embodiments.
[0015] FIG. 9 illustrates another NSI resource allocation
optimization in accordance with some embodiments.
DETAILED DESCRIPTION
[0016] The following description and the drawings sufficiently
illustrate specific embodiments to enable those skilled in the art
to practice them. Other embodiments may incorporate structural,
logical, electrical, process, and other changes. Portions and
features of some embodiments may be included in, or substituted
for, those of other embodiments. Embodiments set forth in the
claims encompass all available equivalents of those claims.
[0017] FIG. 1A illustrates an architecture of a network in
accordance with some aspects. The network 140A includes 3GPP LTE/4G
and NG network functions. A network function can be implemented as
a discrete network element on a dedicated hardware, as a software
instance running on dedicated hardware, and/or as a virtualized
function instantiated on an appropriate platform, e.g., dedicated
hardware or a cloud infrastructure.
[0018] The network 140A is shown to include user equipment (UE) 101
and UE 102. The UEs 101 and 102 are illustrated as smartphones
(e.g., handheld touchscreen mobile computing devices connectable to
one or more cellular networks) but may also include any mobile or
non-mobile computing device, such as portable (laptop) or desktop
computers, wireless handsets, drones, or any other computing device
including a wired and/or wireless communications interface. The UEs
101 and 102 can be collectively referred to herein as UE 101, and
UE 101 can be used to perform one or more of the techniques
disclosed herein.
[0019] Any of the radio links described herein (e.g., as used in
the network 140A or any other illustrated network) may operate
according to any exemplary radio communication technology and/or
standard. Any spectrum management scheme including, for example,
dedicated licensed spectrum, unlicensed spectrum, (licensed) shared
spectrum (such as Licensed Shared Access (LSA) in 2.3-2.4 GHz,
3.4-3.6 GHz, 3.6-3.8 GHz, and other frequencies and Spectrum Access
System (SAS) in 3.55-3.7 GHz and other frequencies). Different
Single Carrier or OFDM modes (CP-OFDM, SC-FDMA, SC-OFDM, filter
bank-based multicarrier (FBMC), OFDMA, etc.), and in particular
3GPP NR, may be used by allocating the OFDM carrier data bit
vectors to the corresponding symbol resources.
[0020] In some aspects, any of the UEs 101 and 102 can comprise an
Internet-of-Things (IoT) UE or a Cellular IoT (CIoT) UE, which can
comprise a network access layer designed for low-power IoT
applications utilizing short-lived UE connections. In some aspects,
any of the UEs 101 and 102 can include a narrowband (NB) IoT UE
(e.g., such as an enhanced NB-IoT (eNB-IoT) UE and Further Enhanced
(FeNB-IoT) UE). An IoT UE can utilize technologies such as
machine-to-machine (M2M) or machine-type communications (MTC) for
exchanging data with an MTC server or device via a public land
mobile network (PLMN), Proximity-Based Service (ProSe) or
device-to-device (D2D) communication, sensor networks, or IoT
networks. The M2M or MTC exchange of data may be a
machine-initiated exchange of data. An IoT network includes
interconnecting IoT UEs, which may include uniquely identifiable
embedded computing devices (within the Internet infrastructure),
with short-lived connections. The IoT UEs may execute background
applications (e.g., keep-alive messages, status updates, etc.) to
facilitate the connections of the IoT network. In some aspects, any
of the UEs 101 and 102 can include enhanced MTC (eMTC) UEs or
further enhanced MTC (FeMTC) UEs.
[0021] The UEs 101 and 102 may be configured to connect, e.g.,
communicatively couple, with a radio access network (RAN) 110. The
RAN 110 may be, for example, an Evolved Universal Mobile
Telecommunications System (UMTS) Terrestrial Radio Access Network
(E-UTRAN), a NextGen RAN (NG RAN), or some other type of RAN.
[0022] The UEs 101 and 102 utilize connections 103 and 104,
respectively, each of which comprises a physical communications
interface or layer (discussed in further detail below); in this
example, the connections 103 and 104 are illustrated as an air
interface to enable communicative coupling, and can be consistent
with cellular communications protocols, such as a Global System for
Mobile Communications (GSM) protocol, a code-division multiple
access (CDMA) network protocol, a Push-to-Talk (PTT) protocol, a
PTT over Cellular (POC) protocol, a Universal Mobile
Telecommunications System (UMTS) protocol, a 3GPP Long Term
Evolution (LTE) protocol, a fifth-generation (5G) protocol, a New
Radio (NR) protocol, and the like.
[0023] In an aspect, the UEs 101 and 102 may further directly
exchange communication data via a ProSe interface 105. The ProSe
interface 105 may alternatively be referred to as a sidelink (SL)
interface comprising one or more logical channels, including but
not limited to a Physical Sidelink Control Channel (PSCCH), a
Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink
Discovery Channel (PSDCH), a Physical Sidelink Broadcast Channel
(PSBCH), and a Physical Sidelink Feedback Channel (PSFCH).
[0024] The UE 102 is shown to be configured to access an access
point (AP) 106 via connection 107. The connection 107 can comprise
a local wireless connection, such as, for example, a connection
consistent with any IEEE 802.11 protocol, according to which the AP
106 can comprise a wireless fidelity (WiFi.RTM.) router. In this
example, the AP 106 is shown to be connected to the Internet
without connecting to the core network of the wireless system
(described in further detail below).
[0025] The RAN 110 can include one or more access nodes that enable
the connections 103 and 104. These access nodes (ANs) can be
referred to as base stations (BSs), NodeBs, evolved NodeBs (eNBs),
Next Generation NodeBs (gNBs), RAN nodes, and the like, and can
comprise ground stations (e.g., terrestrial access points) or
satellite stations providing coverage within a geographic area
(e.g., a cell). In some aspects, the communication nodes 111 and
112 can be transmission/reception points (TRPs). In instances when
the communication nodes 111 and 112 are NodeBs (e.g., eNBs or
gNBs), one or more TRPs can function within the communication cell
of the NodeBs. The RAN 110 may include one or more RAN nodes for
providing macrocells, e.g., macro RAN node 111, and one or more RAN
nodes for providing femtocells or picocells (e.g., cells having
smaller coverage areas, smaller user capacity, or higher bandwidth
compared to macrocells), e.g., low power (LP) RAN node 112.
[0026] Any of the RAN nodes 111 and 112 can terminate the air
interface protocol and can be the first point of contact for the
UEs 101 and 102. In some aspects, any of the RAN nodes 111 and 112
can fulfill various logical functions for the RAN 110 including,
but not limited to, radio network controller (RNC) functions such
as radio bearer management, uplink and downlink dynamic radio
resource management and data packet scheduling, and mobility
management. In an example, any of the nodes 111 and/or 112 can be a
gNB, an eNB, or another type of RAN node.
[0027] The RAN 110 is shown to be communicatively coupled to a core
network (CN) 120 via an S1 interface 113. In aspects, the CN 120
may be an evolved packet core (EPC) network, a NextGen Packet Core
(NPC) network, or some other type of CN (e.g., as illustrated in
reference to FIGS. 1B-1C). In this aspect, the S1 interface 113 is
split into two parts: the S1-U interface 114, which carries traffic
data between the RAN nodes 111 and 112 and the serving gateway
(S-GW) 122, and the S1-mobility management entity (MME) interface
115, which is a signaling interface between the RAN nodes 111 and
112 and MMEs 121.
[0028] In this aspect, the CN 120 comprises the MMEs 121, the S-GW
122, the Packet Data Network (PDN) Gateway (P-GW) 123, and a home
subscriber server (HSS) 124. The MMEs 121 may be similar in
function to the control plane of legacy Serving General Packet
Radio Service (GPRS) Support Nodes (SGSN). The MMEs 121 may manage
mobility aspects in access such as gateway selection and tracking
area list management. The HSS 124 may comprise a database for
network users, including subscription-related information to
support the network entities' handling of communication sessions.
The CN 120 may comprise one or several HSSs 124, depending on the
number of mobile subscribers, on the capacity of the equipment, on
the organization of the network, etc. For example, the HSS 124 can
provide support for routing/roaming, authentication, authorization,
naming/addressing resolution, location dependencies, etc.
[0029] The S-GW 122 may terminate the S1 interface 113 towards the
RAN 110, and routes data packets between the RAN 110 and the CN
120. In addition, the S-GW 122 may be a local mobility anchor point
for inter-RAN node handovers and also may provide an anchor for
inter-3GPP mobility. Other responsibilities of the S-GW 122 may
include a lawful intercept, charging, and some policy
enforcement.
[0030] The P-GW 123 may terminate an SGi interface toward a PDN.
The P-GW 123 may route data packets between the EPC network 120 and
external networks such as a network including the application
server 184 (alternatively referred to as application function (AF))
via an Internet Protocol (IP) interface 125. The P-GW 123 can also
communicate data to other external networks 131A, which can include
the Internet, IP multimedia subsystem (IPS) network, and other
networks. Generally, the application server 184 may be an element
offering applications that use IP bearer resources with the core
network (e.g., UMTS Packet Services (PS) domain, LTE PS data
services, etc.). In this aspect, the P-GW 123 is shown to be
communicatively coupled to an application server 184 via an IP
interface 125. The application server 184 can also be configured to
support one or more communication services (e.g.,
Voice-over-Internet Protocol (VoIP) sessions, PTT sessions, group
communication sessions, social networking services, etc.) for the
UEs 101 and 102 via the CN 120.
[0031] The P-GW 123 may further be a node for policy enforcement
and charging data collection. Policy and Charging Rules Function
(PCRF) 126 is the policy and charging control element of the CN
120. In a non-roaming scenario, in some aspects, there may be a
single PCRF in the Home Public Land Mobile Network (HPLMN)
associated with a UE's Internet Protocol Connectivity Access
Network (IP-CAN) session. In a roaming scenario with a local
breakout of traffic, there may be two PCRFs associated with a UE's
IP-CAN session: a Home PCRF (H-PCRF) within an HPLMN and a Visited
PCRF (V-PCRF) within a Visited Public Land Mobile Network (VPLMN).
The PCRF 126 may be communicatively coupled to the application
server 184 via the P-GW 123.
[0032] In some aspects, the communication network 140A can be an
IoT network or a 5G network, including 5G new radio network using
communications in the licensed (5G NR) and the unlicensed (5G NR-U)
spectrum. One of the current enablers of IoT is the narrowband-IoT
(NB-IoT). Operation in the unlicensed spectrum may include dual
connectivity (DC) operation and the standalone LTE system in the
unlicensed spectrum, according to which LTE-based technology solely
operates in unlicensed spectrum without the use of an "anchor" in
the licensed spectrum, called MulteFire. Further enhanced operation
of LTE systems in the licensed as well as unlicensed spectrum is
expected in future releases and 5G systems. Such enhanced
operations can include techniques for sidelink resource allocation
and UE processing behaviors for NR sidelink V2X communications.
[0033] An NG system architecture can include the RAN 110 and a 5G
network core (5GC) 120. The NG-RAN 110 can include a plurality of
nodes, such as gNBs and NG-eNBs. The core network 120 (e.g., a 5G
core network or 5GC) can include an access and mobility function
(AMF) and/or a user plane function (UPF). The AMF and the UPF can
be communicatively coupled to the gNBs and the NG-eNBs via NG
interfaces. More specifically, in some aspects, the gNBs and the
NG-eNBs can be connected to the AMF by NG-C interfaces, and to the
UPF by NG-U interfaces. The gNBs and the NG-eNBs can be coupled to
each other via Xn interfaces.
[0034] In some aspects, the NG system architecture can use
reference points between various nodes as provided by 3GPP
Technical Specification (TS) 23.501 (e.g., V15.4.0, 2018 December).
In some aspects, each of the gNBs and the NG-eNBs can be
implemented as a base station, a mobile edge server, a small cell,
a home eNB, and so forth. In some aspects, a gNB can be a master
node (MN) and NG-eNB can be a secondary node (SN) in a 5G
architecture.
[0035] FIG. 1B illustrates a non-roaming 5G system architecture in
accordance with some aspects. In particular, FIG. 1B illustrates a
5G System architecture 140B in a reference point representation.
More specifically, UE 102 can be in communication with RAN 110 as
well as one or more other 5GC network entities. The 5G system
architecture 140B includes a plurality of network functions (NFs),
such as an AMF 132, session management function (SMF) 136, policy
control function (PCF) 148, application function (AF) 150, UPF 134,
network slice selection function (NSSF) 142, authentication server
function (AUSF) 144, and unified data management (UDM)/home
subscriber server (HSS) 146.
[0036] The UPF 134 can provide a connection to a data network (DN)
152, which can include, for example, operator services, Internet
access, or third-party services. The AMF 132 can be used to manage
access control and mobility and can also include network slice
selection functionality. The AMF 132 may provide UE-based
authentication, authorization, mobility management, etc., and may
be independent of the access technologies. The SMF 136 can be
configured to set up and manage various sessions according to
network policy. The SMF 136 may thus be responsible for session
management and allocation of IP addresses to UEs. The SMF 136 may
also select and control the UPF 134 for data transfer. The SMF 136
may be associated with a single session of a UE 101 or multiple
sessions of the UE 101. This is to say that the UE 101 may have
multiple 5G sessions. Different SMFs may be allocated to each
session. The use of different SMFs may permit each session to be
individually managed. As a consequence, the functionalities of each
session may be independent of each other.
[0037] The UPF 134 can be deployed in one or more configurations
according to the desired service type and may be connected with a
data network. The PCF 148 can be configured to provide a policy
framework using network slicing, mobility management, and roaming
(similar to PCRF in a 4G communication system). The UDM can be
configured to store subscriber profiles and data (similar to an HSS
in a 4G communication system).
[0038] The AF 150 may provide information on the packet flow to the
PCF 148 responsible for policy control to support a desired QoS.
The PCF 148 may set mobility and session management policies for
the UE 101. To this end, the PCF 148 may use the packet flow
information to determine the appropriate policies for proper
operation of the AMF 132 and SMF 136. The AUSF 144 may store data
for UE authentication.
[0039] In some aspects, the 5G system architecture 140B includes an
IP multimedia subsystem (IMS) 168B as well as a plurality of IP
multimedia core network subsystem entities, such as call session
control functions (CSCFs). More specifically, the IMS 168B includes
a CSCF, which can act as a proxy CSCF (P-CSCF) 162BE, a serving
CSCF (S-CSCF) 164B, an emergency CSCF (E-CSCF) (not illustrated in
FIG. 1B), or interrogating CSCF (I-CSCF) 166B. The P-CSCF 162B can
be configured to be the first contact point for the UE 102 within
the IM subsystem (IMS) 168B. The S-CSCF 164B can be configured to
handle the session states in the network, and the E-CSCF can be
configured to handle certain aspects of emergency sessions such as
routing an emergency request to the correct emergency center or
PSAP. The I-CSCF 166B can be configured to function as the contact
point within an operator's network for all IMS connections destined
to a subscriber of that network operator, or a roaming subscriber
currently located within that network operator's service area. In
some aspects, the I-CSCF 166B can be connected to another IP
multimedia network 170E, e.g. an IMS operated by a different
network operator.
[0040] In some aspects, the UDM/HSS 146 can be coupled to an
application server 160E, which can include a telephony application
server (TAS) or another application server (AS). The AS 160B can be
coupled to the IMS 168B via the S-CSCF 164B or the I-CSCF 166B.
[0041] A reference point representation shows that interaction can
exist between corresponding NF services. For example, FIG. 1B
illustrates the following reference points: N1 (between the UE 102
and the AMF 132), N2 (between the RAN 110 and the AMF 132). N3
(between the RAN 110 and the UPF 134), N4 (between the SMF 136 and
the UPF 134), N5 (between the PCF 148 and the AF 150, not shown).
N6 (between the UPF 134 and the DN 152), N7 (between the SMF 136
and the PCF 148, not shown), N8 (between the UDM 146 and the AMF
132, not shown), N9 (between two UPFs 134, not shown), N10 (between
the UDM 146 and the SMF 136, not shown), N11 (between the AMF 132
and the SMF 136, not shown), N12 (between the AUSF 144 and the AMF
132, not shown), N13 (between the AUSF 144 and the UDM 146, not
shown), N14 (between two AMFs 132, not shown), N15 (between the PCF
148 and the AMF 132 in case of a non-roaming scenario, or between
the PCF 148 and a visited network and AMF 132 in case of a roaming
scenario, not shown), N16 (between two SMFs, not shown), and N22
(between AMF 132 and NSSF 142, not shown). Other reference point
representations not shown in FIG. 1E can also be used.
[0042] FIG. 1C illustrates a 5G system architecture 140C and a
service-based representation. In addition to the network entities
illustrated in FIG. 1B, system architecture 140C can also include a
network exposure function (NEF) 154 and a network repository
function (NRF) 156. In some aspects, 5G system architectures can be
service-based and interaction between network functions can be
represented by corresponding point-to-point reference points Ni or
as service-based interfaces.
[0043] In some aspects, as illustrated in FIG. 1C, service-based
representations can be used to represent network functions within
the control plane that enable other authorized network functions to
access their services. In this regard, 5G system architecture 140C
can include the following service-based interfaces: Namf 158H (a
service-based interface exhibited by the AMF 132), Nsmf 158I (a
service-based interface exhibited by the SMF 136), Nnef 158B (a
service-based interface exhibited by the NEF 154), Npcf 158D (a
service-based interface exhibited by the PCF 148), a Nudm 158E (a
service-based interface exhibited by the UDM 146), Naf 158F (a
service-based interface exhibited by the AF 150), Nnrf 158C (a
service-based interface exhibited by the NRF 156), Nnssf 158A (a
service-based interface exhibited by the NSSF 142), Nausf 158G (a
service-based interface exhibited by the AUSF 144). Other
service-based interfaces (e.g., Nudr, N5g-eir, and Nudsf) not shown
in FIG. 1C can also be used.
[0044] NR-V2X architectures may support high-reliability low
latency sidelink communications with a variety of traffic patterns,
including periodic and aperiodic communications with random packet
arrival time and size. Techniques disclosed herein can be used for
supporting high reliability in distributed communication systems
with dynamic topologies, including sidelink NR V2X communication
systems.
[0045] FIG. 2 illustrates a block diagram of a communication device
in accordance with some embodiments. The communication device 200
may be a UE such as a specialized computer, a personal or laptop
computer (PC), a tablet PC, or a smart phone, dedicated network
equipment such as an eNB, a server running software to configure
the server to operate as a network device, a virtual device, or any
machine capable of executing instructions (sequential or otherwise)
that specify actions to be taken by that machine. For example, the
communication device 200 may be implemented as one or more of the
devices shown in FIG. 1. Note that communications described herein
may be encoded before transmission by the transmitting entity
(e.g., UE, gNB) for reception by the receiving entity (e.g., gNB,
UE) and decoded after reception by the receiving entity.
[0046] Examples, as described herein, may include, or may operate
on, logic or a number of components, modules, or mechanisms.
Modules and components are tangible entities (e.g., hardware)
capable of performing specified operations and may be configured or
arranged in a certain manner. In an example, circuits may be
arranged (e.g., internally or with respect to external entities
such as other circuits) in a specified manner as a module. In an
example, the whole or part of one or more computer systems (e.g., a
standalone, client or server computer system) or one or more
hardware processors may be configured by firmware or software
(e.g., instructions, an application portion, or an application) as
a module that operates to perform specified operations. In an
example, the software may reside on a machine readable medium. In
an example, the software, when executed by the underlying hardware
of the module, causes the hardware to perform the specified
operations.
[0047] Accordingly, the term "module" (and "component") is
understood to encompass a tangible entity, be that an entity that
is physically constructed, specifically configured (e.g.,
hardwired), or temporarily (e.g., transitorily) configured (e.g.,
programmed) to operate in a specified manner or to perform part or
all of any operation described herein. Considering examples in
which modules are temporarily configured, each of the modules need
not be instantiated at any one moment in time. For example, where
the modules comprise a general-purpose hardware processor
configured using software, the general-purpose hardware processor
may be configured as respective different modules at different
times. Software may accordingly configure a hardware processor, for
example, to constitute a particular module at one instance of time
and to constitute a different module at a different instance of
time.
[0048] The communication device 200 may include a hardware
processor (or equivalently processing circuitry) 202 (e.g., a
central processing unit (CPU), a GPU, a hardware processor core, or
any combination thereof), a main memory 204 and a static memory
206, some or all of which may communicate with each other via an
interlink (e.g., bus) 208. The main memory 204 may contain any or
all of removable storage and non-removable storage, volatile memory
or non-volatile memory. The communication device 200 may further
include a display unit 210 such as a video display, an alphanumeric
input device 212 (e.g., a keyboard), and a user interface (UI)
navigation device 214 (e.g., a mouse). In an example, the display
unit 210, input device 212 and UI navigation device 214 may be a
touch screen display. The communication device 200 may additionally
include a storage device (e.g., drive unit) 216, a signal
generation device 218 (e.g., a speaker), a network interface device
220, and one or more sensors, such as a global positioning system
(GPS) sensor, compass, accelerometer, or other sensor. The
communication device 200 may further include an output controller,
such as a serial (e.g., universal serial bus (USB), parallel, or
other wired or wireless (e.g., infrared (IR), near field
communication (NFC), etc.) connection to communicate or control one
or more peripheral devices (e.g., a printer, card reader,
etc.).
[0049] The storage device 216 may include a non-transitory machine
readable medium 222 (hereinafter simply referred to as machine
readable medium) on which is stored one or more sets of data
structures or instructions 224 (e.g., software) embodying or
utilized by any one or more of the techniques or functions
described herein. The instructions 224 may also reside, completely
or at least partially, within the main memory 204, within static
memory 206, and/or within the hardware processor 202 during
execution thereof by the communication device 200. While the
machine readable medium 222 is illustrated as a single medium, the
term "machine readable medium" may include a single medium or
multiple media (e.g., a centralized or distributed database, and/or
associated caches and servers) configured to store the one or more
instructions 224.
[0050] The term "machine readable medium" may include any medium
that is capable of storing, encoding, or carrying instructions for
execution by the communication device 200 and that cause the
communication device 200 to perform any one or more of the
techniques of the present disclosure, or that is capable of
storing, encoding or carrying data structures used by or associated
with such instructions. Non-limiting machine readable medium
examples may include solid-state memories, and optical and magnetic
media. Specific examples of machine readable media may include:
non-volatile memory, such as semiconductor memory devices (e.g.,
Electrically Programmable Read-Only Memory (EPROM), Electrically
Erasable Programmable Read-Only Memory (EEPROM)) and flash memory
devices; magnetic disks, such as internal hard disks and removable
disks: magneto-optical disks; Radio access Memory (RAM); and CD-ROM
and DVD-ROM disks.
[0051] The instructions 224 may further be transmitted or received
over a communications network using a transmission medium 226 via
the network interface device 220 utilizing any one of a number of
wireless local area network (WLAN) transfer protocols (e.g., frame
relay, internet protocol (IP), transmission control protocol (TCP),
user datagram protocol (UDP), hypertext transfer protocol (HTTP),
etc.). Example communication networks may include a local area
network (LAN), a wide area network (WAN), a packet data network
(e.g., the Internet), mobile telephone networks (e.g., cellular
networks), Plain Old Telephone (POTS) networks, and wireless data
networks. Communications over the networks may include one or more
different protocols, such as Institute of Electrical and
Electronics Engineers (IEEE) 802.11 family of standards known as
Wi-Fi, IEEE 802.16 family of standards known as WiMax. IEEE
802.15.4 family of standards, a Long Term Evolution (LTE) family of
standards, a Universal Mobile Telecommunications System (UMTS)
family of standards, peer-to-peer (P2P) networks, a next generation
(NG)/5.sup.th generation (5G) standards among others. In an
example, the network interface device 220 may include one or more
physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or
more antennas to connect to the transmission medium 226.
[0052] Note that the term "circuitry" as used herein refers to, is
part of, or includes hardware components such as an electronic
circuit, a logic circuit, a processor (shared, dedicated, or group)
and/or memory (shared, dedicated, or group), an Application
Specific Integrated Circuit (ASIC), a field-programmable device
(FPD) (e.g., a field-programmable gate array (FPGA), a programmable
logic device (PLD), a complex PLD (CPLD), a high-capacity PLD
(HCPLD), a structured ASIC, or a programmable SoC), digital signal
processors (DSPs), etc., that are configured to provide the
described functionality. In some embodiments, the circuitry may
execute one or more software or firmware programs to provide at
least some of the described functionality. The term "circuitry" may
also refer to a combination of one or more hardware elements (or a
combination of circuits used in an electrical or electronic system)
with the program code used to carry out the functionality of that
program code. In these embodiments, the combination of hardware
elements and program code may be referred to as a particular type
of circuitry.
[0053] The term "processor circuitry" or "processor" as used herein
thus refers to, is part of, or includes circuitry capable of
sequentially and automatically carrying out a sequence of
arithmetic or logical operations, or recording, storing, and/or
transferring digital data. The term "processor circuitry" or
"processor" may refer to one or more application processors, one or
more baseband processors, a physical central processing unit (CPU),
a single- or multi-core processor, and/or any other device capable
of executing or otherwise operating computer-executable
instructions, such as program code, software modules, and/or
functional processes.
[0054] FIG. 3 illustrates an NFV network management architecture in
accordance with some embodiments. As illustrated, the NFV network
management architecture 300 may include a number of elements (each
of which may contain physical and/or virtualized components),
including a Network Virtualization Function Infrastructure (NVFI)
310, Network elements (NEs) 390, Virtual Network Functions (VNFs)
320, a Domain Manager (DM) 330, an Element Manager (EM) 332, a
Network Manager (NM) 342, and an NFV Management and Orchestration
(NFV-MANO) 380. The NFV-MANO 380, which may be replaced as
indicated herein by multiple NFV-MANO, may comprise a Virtualized
Infrastructure Manager (VIM) 340, a VNF Manager (VNFM) 350, and a
Network Function Virtualization Orchestrator (NFVO) 360. The NM 342
may be contained in an Operations Support System/Business Support
System (OSS/BSS) 340, with the DM 330 and NM 342 forming the 3GPP
management system 334.
[0055] The NFV network management architecture 300 may be
implemented by, for example, a data center comprising one or more
servers in the cloud. The NFV network management architecture 300,
in some embodiments, may include one or more physical devices
and/or one or more applications hosted on a distributed computing
platform, a cloud computing platform, a centralized hardware
system, a server, a computing device, and/or an external
network-to-network interface device, among others. In some cases,
the virtualized resource performance measurement may include, for
example, latency, jitter, bandwidth, packet loss, nodal
connectivity, compute, network, and/or storage resources,
accounting, fault and/or security measurements. In particular, the
NEs 390 may comprise physical network functions (PNF) including
both hardware such as processors, antennas, amplifiers, transmit
and receive chains, as well as software. The VNFs 320 may be
instantiated in one or more servers. Each of the VNFs 320, DM 330
and the NEs 390 may contain an EM 322, 332, 392.
[0056] The NFV Management and Orchestration (NFV-MANO) 380 may
manage the NFVI 310. The NFV-MANO 380 may orchestrate the
instantiation of network services, and the allocation of resources
used by the VNFs 320. The NFV-MANO 380 may, along with the OSS/BSS
340, be used by external entities to deliver various NFV business
benefits. The OSS/BSS 340 may include the collection of systems and
management applications that a service provider may use to operate
their business: management of customers, ordering, products and
revenues--for example, payment or account transactions, as well as
telecommunications network components and supporting processes
including network component configuration, network service
provisioning and fault handling. The NFV-MANO 380 may create or
terminate a VNF 320, increase or decrease the VNF capacity, or
update or upgrade software and/or configuration of a VNF. The
NFV-MANO 380 may have access to various data repositories including
network services, VNFs available, NFV instances and NFVI resources
with which to determine resource allocation.
[0057] The VIM 340 may control and manage the NFVI resources via
Nf-Vi reference points within the infrastructure sub-domain. The
VIM 340 may further collect and forward performance measurements
and events to the VNFM 350 via Vi-VNFM and to the NFVO 360 via
Or-Vi reference points. The NFVO 360 may be responsible for
managing new VNFs and other network services, including lifecycle
management of different network services, which may include VNF
instances, global resource management, validation and authorization
of NFVI resource requests and policy management for various network
services. The NFVO 360 may coordinate VNFs 320 as part of network
services that jointly realize a more complex function, including
joint instantiation and configuration, configuring required
connections between different VNFs 320, and managing dynamic
changes of the configuration. The NFVO 360 may provide this
orchestration through an OS-Ma-NFVO reference point with the NM
342. The VNFM 350 may orchestrate NFVI resources via the VIM 370
and provide overall coordination and adaptation for configuration
and event reporting between the VIM 320 and the EMs and NMs. The
former may involve discovering available services, managing
virtualized resource availability/allocation/release and providing
virtualized resource fault/performance management. The latter may
involve lifecycle management that may include instantiating a VNF,
scaling and updating the VNF instances, and terminating the network
service, releasing the NFVI resources for the service to the NFVI
resource pool to be used by other services.
[0058] The VIM 370 may be specialized in handling a certain type of
NFVI resource (e.g. compute-only, storage-only, networking-only),
or may be capable of managing multiple types of NFVI resources. The
VIM 340 may, among others, orchestrate the
allocation/upgrade/release/reclamation of NFVI resources (including
the optimization of such resources usage) and manage the
association of the virtualized resources to the physical compute,
storage, networking resources, and manage repository
inventory-related information of NFVI hardware resources (compute,
storage, networking) and software resources (e.g. hypervisors), and
discovery of the capabilities and features (e.g. related to usage
optimization) of such resources.
[0059] The VNFM 350 may be responsible for the lifecycle management
of the VNFs 320 via the Ve-VNFM-VNF reference point and may
interface to EMs 322, 332 through the Ve-VNFM-EM reference point.
The VNFM 350 may be assigned the management of a single VNF 320, or
the management of multiple VNFs 320 of the same type or of
different types. Thus, although only one VNFM 350 is shown in FIG.
3, different VNFMs 350 may be associated with the different VNFs
320 for performance measurement and other responsibilities. The
VNFM 350 may provide a number of VNF functionalities, including
instantiation (and configuration if required by the VNF deployment
template), software update/upgrade, modification, scaling out/in
and up/down, collection of NFVI performance measurement results and
faults/events information and correlation to VNF instance-related
events/faults, healing, termination, lifecycle management change
notification, integrity management, and event reporting.
[0060] The NVFI 310 may itself contain various virtualized and
non-virtualized resources. These may include a plurality of virtual
machines (VMs) that may provide computational abilities (CPU), one
or more memories that may provide storage at either block or
file-system level and one or more networking elements that may
include networks, subnets, ports, addresses, links and forwarding
rules to ensure intra- and inter-VNF connectivity.
[0061] Each VNF 320 may provide a network function that is
decoupled from infrastructure resources (computational resources,
networking resources, memory) used to provide the network function.
Although not shown, the VNFs 320 can be chained with other VNFs 320
and/or other physical network function to realize a network
service. The virtualized resources may provide the VNFs 320 with
desired resources. Resource allocation in the NFVI 310 may
simultaneously meet numerous requirements and constraints, such as
low latency or high bandwidth links to other communication
endpoints.
[0062] The VNFs 320, like the NEs 390 may be managed by one or more
EMs 322, 332, 392. The EM may provide functions for management of
virtual or physical network elements, depending on the
instantiation. The EM may manage individual network elements and
network elements of a sub-network, which may include relations
between the network elements. For example, the EM 322 of a VNF 320
may be responsible for configuration for the network functions
provided by a VNF 320, fault management for the network functions
provided by the VNF 320, accounting for the usage of VNF functions,
and collecting performance measurement results for the functions
provided by the VNF 320.
[0063] The EMs 322, 332, 392 (whether in a VNF 320 or NE 390) may
be managed by the NM 342 of the OSS/BSS 340 through Itf-N reference
points. The NM 342 may provide functions with the responsibility
for the management of a network, mainly as supported by the EM 332
but may also involve direct access to the network elements. The NM
342 may connect and disconnect VNF external interfaces to physical
network function interfaces at the request of the NFVO 360.
[0064] As above, the various components of the system may be
connected through different reference points. The references points
between the NFV-MANO 380 and the functional blocks of the system
may include an Os-Ma-NFVO between the NM 342 and NFVO 360, a
Ve-VNFM-EM between the EM 322, 332 and the VNFM 350, a Ve-VNFM-VNF
between a VNF 320 and the VNFM 350, a Nf-Vi between the NFVI 310
and the VIM 340, an Or-VNFM between the NFVO 360 and the VNFM 350,
an Or-Vi between the NFVO 360 and the VIM 340, and a Vi-VNFM
between the VIM 340 and the VNFM 350. An Or-Vi interface may
implement the VNF software image management interface and
interfaces for the management of virtualized resources, their
catalogue, performance and failure on the Or-Vi reference point. An
Or-Vnfm interface may implement a virtualized resource management
interface on the Or-Vnfm reference point. A Ve-Vnfm interface may
implement a virtualized resource performance/fault management on
the Ve-Vnfm reference point.
[0065] As above, with the advent of 5G, networks and disparate
devices (such as Machine Type Communication (MTC), enhanced Mobile
Broadband (eMBB) and Ultra-Reliable and Low Latency Communications
(URLLC) devices) using these networks have become increasingly
complex and richer and more demanding applications such as URLLC
for autonomous driving have evolved. Self-driving networks are
being developed to support autonomous driving, including the use of
artificial intelligence/machine learning (AI/ML) to embed
intelligence in the RAN architecture. In particular, an intelligent
RAN Controller is likely to play an important role in such
networks. SON functionality may be able to be realized in the
Near-Real Time RAN Intelligent Controller (RIC) to support the open
RAN (O-RAN) alliance vision of self-driving networks.
[0066] While various SON functions running on a RIC may be
developed, the embodiments herein in particular, relate to Random
Access Channel (RACH) optimization for Near Real-Time RIC and
Network Slice Instance (NSI) resource allocation optimization for
Non-Real Time RIC, as well as methods of Load Balancing
Optimization (LBO) and Mobility Robustness Optimization (MRO).
[0067] FIG. 4 shows a SON function over RIC in accordance with some
embodiments. For example, the functionality described may be to
provide NSSI resource allocation optimization. The SON algorithms
running in the Near-Real Time RIC may receive information from the
Non Real-Time RIC. The Near-Real Time RIC may be implemented as a
managed function. The Non Real-Time RIC may be implemented within
the service management and orchestration framework. The information
may include, for example, profiles and SON targets among others.
The SON algorithms may interact with network functions in the RAN
through an Operation, Administration, and Management (OAM)
interface. As shown in FIG. 4, the network functions may include an
O-RAN central unit user plane (O-CU-UP) and an O-RAN central unit
control plane (O-CU-CP) connected via an E1 interface. The O-CU-UP
and O-CU-CP may be in communication with an O-RAN distributed unit
(O-DU) via an F1-U interface and an F1-C interface, respectfully.
The O-DU may be in communication with an O-RAN remote unit (O-RU)
via an open fronthaul interface. The O-CU-UP, O-CU-CP, O-DU, and
O-RU may implement the functionality of the O-RAN for communication
with a UE. The Non Real-Time RIC may also be connected with the
infrastructure management framework, which includes a VIM, and the
infrastructure management framework may be connected with other
infrastructure via an NFVI.
[0068] The SON algorithms may include a number of operations such
as monitoring, analysis/decision making, execution, and evaluation.
During monitoring the radio network(s) may be monitored by
collecting data via the O1 interface and storing the data in
memory. The data may include performance measurements, alarms
(e.g., radio link failure), analytic data, and other information,
from the network functions. For example, the measurements may be
measured per NSSI for, e.g., performance measurements may include
the downlink (DL) and uplink (UL) physical resource blocks (PRBs)
used for data traffic, average DL and UL UE throughput in the gNB,
and number of protocol data unit (PDU) sessions requested to setup,
successfully setup and failed to set up.
[0069] During analysis and decision making the data may be analysed
to determine whether there are issues in the network(s) to be
resolved. The data may be analyzed to train the AI/ML model (e.g.,
to predict the traffic demand patterns of NSSI at different times
and locations). The actions may be determined to add or reduce the
resources (e.g., capacity, VNF resources, slice subnet attributes)
for, e.g., the NSSI at a given time and location (which O-RAN
nodes).
[0070] If so, which SON actions to be executed may be determined to
resolve the issues. During the execution phase, the SON algorithm
may interact with network functions to execute the SON actions. For
example, the SON actions execute the actions to reallocate NSSI
resources may include reconfiguring NSSI attributes via the O1
interface and updating cloud resources via an O2 interface. During
the evaluation phase, the performance measurements may be collected
and analysed from the network functions to evaluate whether the
issues have been solved as a result of executing the SON
actions.
[0071] In some cases, the RAN nodes (O-CU-CP, O-CU-UP, O-DU, O-RU)
may both support the performance measurement collection with a
desired granularity over the O1 interface as well as supporting a
configuration related to the NSSI resource allocation update over
the O1 interface.
[0072] FIG. 5 illustrates a RACH optimization in accordance with
some embodiments. In particular, FIG. 5 depicts the RACH
optimization use case to show how the SON described above can be
used to enable the O-RAN RIC to improve the RACH performance over
NR cells.
[0073] The RACH configuration may impact user experience and
overall network performance. The RACH collision probability, access
setup delays, data resuming delays from the uplink (UL)
unsynchronized state, handover delays, transition delays from
RRC_INACTIVE, and beam failure recovery delays are all affected by
the RACH settings. RACH optimization may automatically configure
the RACH parameters, and adapt to the changes in the number of UEs
or resources in a cell in order to achieve the optimal network
performance by reducing the network access time, and minimize the
failures.
[0074] As shown in FIG. 5, at operation 1, the Non Real-Time RIC
may provide the targets of RACH performance for one or more NR
cells to Near-Real Time RIC. At operation 2, the Near-Real Time RIC
may monitor the RACH performance for the one or more NR cells by
collecting performance measurements of the one or more NR cells. At
operation 3, the Near-Real Time RIC may analyse the performance
measurements to decide whether the RACH performance target is met.
At operation 4, the Near-Real Time RIC may decide the SON action to
optimize the RACH performance if the target is not met. At
operation 5, the Near-Real Time RIC may execute the SON action. At
operation 6, the Near-Real Time RIC may collect the performance
measurements from the O-CU-CP(s). At operation 7, the Near-Real
Time RIC may analyse the performance measurements to evaluate
whether the SON action has resolved the RACH performance issues. At
operation 8, the Near-Real Time RIC may decide further SON action
to resolve the RACH performance issues if the issues are determined
to not be resolved and may subsequently at operation 9 execute the
further SON action. In some embodiments, the operations (decision,
execution, collection, analysis) may continue to loop until the
issues are resolved.
[0075] For NSI resource allocation optimization, as 5G networks use
various new services are deemed to have sporadic traffic patterns,
where internet of Things (IoT) applications, such as smart meters,
tend to run during off-peak hours or weekends, special events, such
as sport games, concerts, can cause traffic demand to rapidly
increase at certain time and locations. Therefore, traditional
network traffic management is not able to deal with the dynamic
changes of network traffic demand.
[0076] In this case, AI/ML may be used to leverage to analyze huge
number of historical data collected over an extended time period of
days, weeks, months and beyond from the RAN nodes to predict the
traffic demands patterns of 5G networks in different times and
locations. It may then automatically re-allocate the network
resources ahead of the network issues surfaced. This shows that the
SON function running in the Non-Real Time RIC can support NSI
resource allocation optimization.
[0077] FIG. 6 illustrates the realization of a SON function on the
Non Real-Time RIC in accordance with some embodiments. The elements
are similar to those shown in FIG. 4, however, the operations may
be performed by the Non Real-Time RIC rather than the Real-Time
RIC. The realization may include the following operations:
[0078] The SON algorithms may include a number of operations such
as monitoring, analysis/decision making, and execution. During
monitoring the radio network(s) may be monitored by collecting
data, including performance measurements, alarms (e.g., radio link
failure), analytic data, and other information, from the network
functions. During analysis and decision making the data may be
analysed to determine whether there are issues in the network(s) to
be resolved. If so, which SON actions to be executed may be
determined to resolve the issues. During the execution phase, the
SON algorithm may interact with network functions to execute the
SON actions.
[0079] FIG. 7 illustrates NSI resource allocation optimization in
accordance with some embodiments. The NSI resource allocation
optimization use case shows how the SON concept of FIG. 6 can be
used to enable Non-real Time RIC to optimize NSI resource
allocation. At operation 1, the Non Real-Time RIC may collect the
performance measurements (e.g. data volume, the number of
registered UEs, the number of packet data unit (PDU) sessions, . .
. etc) for NSIs supporting the eMBB, URLLC, and mMTC services. At
operation 2, the Non Real-Time RIC may analyse the performance
measurements, including huge number of historical data collected
over days, weeks, months and beyond, to monitor the usage patterns,
and generate or train the AI/ML model related to NSI resource
allocation. At operation 3, based on the AI/ML model, the Non
Real-Time RIC may predict the traffic demand for each NSI for a
given time and location, and decide if it is to optimize NSI
resource allocation. If optimization is to occur, at operation 4,
the Non Real-Time RIC may decide the SON action to optimize the NSI
resource allocation that includes the selection of the NR cells
affected by the NSI resource allocation, and the configuration
changes to be undertaken. At operation 5, the Non Real-Time RIC may
execute the SON action to request O-CU-CP(s) to adjust the resource
allocation (e.g. addition, reduction) for the NSIs in the selected
one or more NR cells at the given times and make configuration
changes.
[0080] FIG. 8 illustrates another realization of a SON function on
the Non Real-Time RIC in accordance with some embodiments. The
elements are similar to those shown in FIGS. 4 and 6, however, the
operations may be performed by both the Non Real-Time RIC and the
Real-Time RIC. Similar to the above, the SON algorithms may include
a number of operations such as monitoring, analysis/decision
making, execution, and evaluation as described above. While in some
embodiments the monitoring and analysis and decision may be solely
within the jurisdiction of the Non Real-Time RIC, both the Non
Real-Time RIC and the Real-Time RIC may be involved in the
execution phase.
[0081] FIG. 9 illustrates another NSI resource allocation
optimization in accordance with some embodiments. The NSI resource
allocation optimization use case in FIG. 9 shows the manner in
which the SON algorithm as described above in relation to FIG. 8
can be used to enable Non-Real Time RIC to optimize NSI resource
allocation.
[0082] At operation 1, the Non Real-Time RIC may collect the
performance measurements (e.g. data volume, the number of
registered UEs, the number of PDU sessions, . . . etc) for NSIs
supporting the desired services, such as eMBB, URLLC, and/or mMTC
services. At operation 2, the Non Real-Time RIC may analyze the
performance measurements, including substantial amount of
historical data collected over, e.g., days, weeks, months and
beyond, to monitor the usage patterns, and generate or train the
AI/ML model related to NSI resource allocation. At operation 3,
based on the AI/ML model, the Non Real-Time RIC may predict the
traffic demand for each NSI for a given time and location. The Non
Real-Time RIC may then decide if optimization of the NSI resource
allocation is to occur. At operation 4, in response to the Non
Real-Time RIC deciding that optimization of the NSI resource
allocation is to occur, the Non Real-Time RIC may determine the SON
action to optimize the NSI resource allocation. After this
determination, at operation 5 the Non Real-Time RIC may execute the
SON action to request the Near-Real Time RIC to optimize NSI
resource allocation at one or more given times and/or locations. At
operation 6, in response to reception of the request from the Non
Real-Time RIC, the Near-Real Time RIC may determine the one or more
NR cells to be affected by the NSI resource allocation, as well as
one or more configuration changes for each NR cell. At operation 7,
after making the NR cell determination, the Near-Real Time RIC may
execute the configuration changes to adjust the resource allocation
(e.g. addition, reduction) for the NSIs.
[0083] Although an embodiment has been described with reference to
specific example embodiments, it will be evident that various
modifications and changes may be made to these embodiments without
departing from the broader scope of the present disclosure.
Accordingly, the specification and drawings are to be regarded in
an illustrative rather than a restrictive sense. The accompanying
drawings that form a part hereof show, by way of illustration, and
not of limitation, specific embodiments in which the subject matter
may be practiced. The embodiments illustrated are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed herein. Other embodiments may be utilized
and derived therefrom, such that structural and logical
substitutions and changes may be made without departing from the
scope of this disclosure. This Detailed Description, therefore, is
not to be taken in a limiting sense, and the scope of various
embodiments is defined only by the appended claims, along with the
full range of equivalents to which such claims are entitled.
[0084] The subject matter may be referred to herein, individually
and/or collectively, by the term "embodiment" merely for
convenience and without intending to voluntarily limit the scope of
this application to any single inventive concept if more than one
is in fact disclosed. Thus, although specific embodiments have been
illustrated and described herein, it should be appreciated that any
arrangement calculated to achieve the same purpose may be
substituted for the specific embodiments shown. This disclosure is
intended to cover any and all adaptations or variations of various
embodiments. Combinations of the above embodiments, and other
embodiments not specifically described herein, will be apparent to
those of skill in the art upon reviewing the above description.
[0085] In this document, the terms "a" or "an" are used, as is
common in patent documents, to include one or more than one,
independent of any other instances or usages of "at least one" or
"one or more." In this document, the term "or" is used to refer to
a nonexclusive or, such that "A or B" includes "A but not B," "B
but not A," and "A and B," unless otherwise indicated. In this
document, the terms "including" and "in which" are used as the
plain-English equivalents of the respective terms "comprising" and
"wherein." Also, in the following claims, the terms "including" and
"comprising" are open-ended, that is, a system, UE, article,
composition, formulation, or process that includes elements in
addition to those listed after such a term in a claim are still
deemed to fall within the scope of that claim. Moreover, in the
following claims, the terms "first," "second." and "third," etc.
are used merely as labels, and are not intended to impose numerical
requirements on their objects.
[0086] The Abstract of the Disclosure is provided to comply with 37
C.F.R. .sctn. 1.72(b), requiring an abstract that will allow the
reader to quickly ascertain the nature of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. In addition,
in the foregoing Detailed Description, it can be seen that various
features are grouped together in a single embodiment for the
purpose of streamlining the disclosure. This method of disclosure
is not to be interpreted as reflecting an intention that the
claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter lies in less than all features of a single
disclosed embodiment. Thus, the following claims are hereby
incorporated into the Detailed Description, with each claim
standing on its own as a separate embodiment.
* * * * *